BL-WoLF：零和博弈中损失有界可学习性的框架
BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games
摘要：
我们提出了BL-WoLF，在重复的零和游戏中的可学习性框架，其中学习的成本是由学习代理累积的损失（而不是轮数）来衡量的。这个游戏是从学习者知道的一些家庭中选择的。对手知道游戏和学习者的学习策略。学习者试图要么不累积损失，要么快速学习游戏以避免未来的损失（这与赢或快速学习（WoLF）原则一致; BL代表“有限损失”）。我们的框架允许概率和近似学习。由此产生的{ \em BL-WoLF}-可学习性的概念可以应用于任何类别的游戏，并允许我们测量对于不知道它在哪类游戏中的玩家的固有劣势。我们提出了{ \em保证BL-WoLF-learnability}结果的家庭的游戏具有确定性的回报和家庭的游戏具有随机的回报。我们证明，这些家庭是{ \em保证约BL-WoLF-learnable}具有较低的成本。然后，我们展示了不保证BL-WoLF-learnable的游戏家族（随机和确定性）。然而，我们证明了这些家族是{ \em BL-WoLF-learnable}的。为了证明这些结果，我们使用一个关键引理，我们得出。
英文摘要：
We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number of rounds). The game is adversarially chosen from some family that the learner knows. The opponent knows the game and the learner's learning strategy. The learner tries to either not accrue losses, or to quickly learn about the game so as to avoid future losses (this is consistent with the Win or Learn Fast (WoLF) principle; BL stands for ``bounded loss''). Our framework allows for both probabilistic and approximate learning. The resultant notion of {\em BL-WoLF}-learnability can be applied to any class of games, and allows us to measure the inherent disadvantage to a player that does not know which game in the class it is in. We present {\em guaranteed BL-WoLF-learnability} results for families of games with deterministic payoffs and families of games with stochastic payoffs. We demonstrate that these families are {\em guaranteed approximately BL-WoLF-learnable} with lower cost. We then demonstrate families of games (both stochastic and deterministic) that are not guaranteed BL-WoLF-learnable. We show that those families, nevertheless, are {\em BL-WoLF-learnable}. To prove these results, we use a key lemma which we derive.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
